# Copyright 2024 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import warnings
from typing import TYPE_CHECKING, Optional, Tuple, List, Union, Literal, Dict, Any

import math
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from torchvision import transforms
from einops import rearrange
from torch.utils.checkpoint import checkpoint

from transformers import PreTrainedModel, PreTrainedTokenizer
from transformers.utils.logging import get_logger
from transformers.activations import ACT2FN
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast

from .configuration_cogvlm import CogVLMConfig
from .rotary_embeddings import RotaryEmbedding  as FastRotaryEmbedding, apply_rotary_pos_emb_index_bhs
from .visual import EVA2CLIPModel

if TYPE_CHECKING:
    from transformers.utils import ModelOutput

logger = get_logger(__name__)

LANGUAGE_TOKEN_TYPE = 0
VISION_TOKEN_TYPE = 1


# Copied from transformers.models.bart.modeling_bart._make_causal_mask
def _make_causal_mask(
        input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
):
    """
    Make causal mask used for bi-directional self-attention.
    """
    bsz, tgt_len = input_ids_shape
    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
    mask_cond = torch.arange(mask.size(-1), device=device)
    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
    mask = mask.to(dtype)

    if past_key_values_length > 0:
        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)


# Copied from transformers.models.bart.modeling_bart._expand_mask
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
    """
    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
    """
    bsz, src_len = mask.size()
    tgt_len = tgt_len if tgt_len is not None else src_len

    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)

    inverted_mask = 1.0 - expanded_mask

    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)


class RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-5):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return (self.weight * hidden_states).to(input_dtype)


class MLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


def get_expert_mask(token_type_ids: "torch.LongTensor(B, L)") -> "[torch.BoolTensor(B, L), torch.BoolTensor(B, L)]":
    vision_token_mask = torch.zeros_like(token_type_ids, dtype=torch.bool)
    vision_token_mask[:, :-1] = (token_type_ids[:, :-1] == VISION_TOKEN_TYPE) & (token_type_ids[:, 1:] == VISION_TOKEN_TYPE)
    language_token_mask = ~vision_token_mask
    return vision_token_mask, language_token_mask


class VisionExpertMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.language_mlp = MLP(config)
        self.vision_mlp = MLP(config)

    def forward(self, hidden_states: "torch.Tensor(B, L, D)", token_type_ids: "torch.LongTensor(B, L)"):
        output = torch.empty(hidden_states.shape, dtype=hidden_states.dtype, device=hidden_states.device)
        vision_token_mask, language_token_mask = get_expert_mask(token_type_ids)
        output[vision_token_mask] = self.vision_mlp(hidden_states[vision_token_mask])
        output[language_token_mask] = self.language_mlp(hidden_states[language_token_mask])
        return output


def attention_fn(
        query_layer: "torch.tensor(B, H, L, HD)",
        key_layer: "torch.tensor(B, H, L, HD)",
        value_layer: "torch.tensor(B, H, L, HD)",
        attention_mask: "torch.tensor(B, H, L, HD)",
        *,
        scaling_attention_score: bool = True,
        attention_dropout: nn.Module = None
):
    attention_mask_bool = (attention_mask == 0)
    is_low_triangle = (attention_mask_bool == torch.ones_like(attention_mask_bool, dtype=torch.float).tril()).all()
    is_full = (attention_mask_bool > 0).all()
    if not (int(torch.__version__.split('.')[0]) >= 2):
        warnings.warn("It's recommended to use torch2.0 or higher.")
    if int(torch.__version__.split('.')[0]) >= 2 and scaling_attention_score and (is_full or is_low_triangle):
        dropout_p = 0. if attention_dropout is None or not attention_dropout.training else attention_dropout.p
        return torch.nn.functional.scaled_dot_product_attention(
            query_layer, key_layer, value_layer,
            attn_mask=None,
            dropout_p=dropout_p,
            is_causal=not is_full
        )
    else:
        if scaling_attention_score:
            query_layer = query_layer / math.sqrt(query_layer.shape[-1])
        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
        attention_scores = attention_scores + attention_mask
        attention_scores = nn.functional.softmax(attention_scores, dim=-1, dtype=torch.float32).to(query_layer.dtype)
        if attention_dropout is not None:
            attention_scores = attention_dropout(attention_scores)
        context_layer = torch.matmul(attention_scores, value_layer)
        return context_layer


class VisionExpertAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.num_attention_heads = config.num_attention_heads
        self.num_multi_query_heads = config.num_multi_query_heads
        self.hidden_size_per_attention_head = self.hidden_size // self.num_attention_heads
        self.stride = [self.num_attention_heads, self.num_multi_query_heads, self.num_multi_query_heads]
        self.qkv_size = self.hidden_size + self.hidden_size_per_attention_head * self.num_multi_query_heads * 2
        self.head_dim = self.hidden_size // self.num_attention_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rotary_emb = FastRotaryEmbedding(self.head_dim)
        self.vision_expert_query_key_value = nn.Linear(self.hidden_size, self.qkv_size, bias=True)
        self.vision_expert_dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        self.language_expert_query_key_value = nn.Linear(self.hidden_size, self.qkv_size, bias=False)
        self.language_expert_dense = nn.Linear(self.hidden_size, self.hidden_size, bias=False)

    def _transpose_for_scores(self, tensor):
        """Transpose a 3D tensor [B, L, H*HD] into a 4D tensor with size [B H L HD]."""
        new_tensor_shape = tensor.size()[:-1] + \
                           (-1, # flexible for multi-query
                            self.hidden_size_per_attention_head)
        tensor = tensor.view(*new_tensor_shape)
        return tensor.permute(0, 2, 1, 3)

    def forward(
            self,
            hidden_states: torch.Tensor,
            token_type_ids: torch.LongTensor,
            position_ids: torch.LongTensor,
            attention_mask: Optional[torch.Tensor] = None,
            past_key_value: Optional[Tuple[torch.Tensor]] = None,
            output_attentions: bool = False,
            use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        bsz, q_len, _ = hidden_states.size()
        vision_token_mask, language_token_mask = get_expert_mask(token_type_ids)

        shape = list(hidden_states.shape)
        shape[-1] = self.qkv_size
        mixed_raw_layer = torch.empty(shape, dtype=hidden_states.dtype, device=hidden_states.device)
        mixed_raw_layer[vision_token_mask] = self.vision_expert_query_key_value(hidden_states[vision_token_mask])
        mixed_raw_layer[language_token_mask] = self.language_expert_query_key_value(hidden_states[language_token_mask])

        # query_states, key_states, value_states = torch.split(mixed_raw_layer, self.hidden_size, dim=-1)
        factor = mixed_raw_layer.size()[-1] // sum(self.stride)
        query_states, key_states, value_states = torch.split(mixed_raw_layer, [factor * x for x in self.stride], dim=-1)

        query_states = self._transpose_for_scores(query_states)  # B, H, L, HD
        key_states = self._transpose_for_scores(key_states)  # B, H, L, HD
        value_states = self._transpose_for_scores(value_states)  # B, H, L, HD

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]

        cos, sin = self.rotary_emb(value_states, seq_len=position_ids.max() + 1)
        query_states, key_states = apply_rotary_pos_emb_index_bhs(query_states, key_states, cos, sin, position_ids)

        if past_key_value is not None:
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)

        past_key_value = (key_states, value_states) if use_cache else None

        key_states = key_states.unsqueeze(2).expand(-1, -1, self.num_attention_heads // self.num_multi_query_heads, -1, -1).contiguous().view(
            bsz, self.num_attention_heads, *key_states.shape[2:])
        value_states = value_states.unsqueeze(2).expand(-1, -1, self.num_attention_heads // self.num_multi_query_heads, -1,
                                                        -1).contiguous().view(bsz, self.num_attention_heads, *value_states.shape[2:])

        context_layer = attention_fn(
            query_layer=query_states, key_layer=key_states, value_layer=value_states, attention_mask=attention_mask,
            scaling_attention_score=True, attention_dropout=None)
        if context_layer.size() != (bsz, self.num_attention_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_attention_heads, q_len, self.head_dim)}, but is"
                f" {context_layer.size()}"
            )
        context_layer = context_layer.transpose(1, 2).contiguous().reshape(bsz, q_len, self.hidden_size)

        attn_output = torch.empty(context_layer.shape, dtype=hidden_states.dtype, device=hidden_states.device)
        attn_output[vision_token_mask] = self.vision_expert_dense(context_layer[vision_token_mask])
        attn_output[language_token_mask] = self.language_expert_dense(context_layer[language_token_mask])

        if output_attentions:
            warnings.warn("output_attentions is not implemented.")

        return attn_output, None, past_key_value


class CogVLMDecoderLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = VisionExpertAttention(config=config)
        self.mlp = VisionExpertMLP(config)
        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
            self,
            hidden_states: torch.Tensor,
            token_type_ids: torch.LongTensor,
            position_ids: torch.LongTensor,
            attention_mask: Optional[torch.Tensor] = None,
            past_key_value: Optional[Tuple[torch.Tensor]] = None,
            output_attentions: Optional[bool] = False,
            use_cache: Optional[bool] = False,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            attention_mask=attention_mask,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states, token_type_ids=token_type_ids)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs  # type: ignore


class CogVLMPreTrainedModel(PreTrainedModel):
    config_class = CogVLMConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = False
    _no_split_modules = ["CogVLMDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()


def is_empty(images_list: Optional[List[List[torch.Tensor]]]):
    if images_list is None or len(images_list) == 0:
        return True
    for image_list in images_list:
        if len(image_list):
            return False
    return True


def build_position_ids(x: "torch.BoolTensor(B, L)", attention_mask: Optional["torch.BoolTensor(B, L)"] = None) -> "torch.LongTensor(B, L)":
    if attention_mask is not None:
        tmp = x.clone()
        tmp[~(attention_mask.bool())] = -1
    else:
        tmp = x.clone()
    # image boi eoi token as LANGUAGE_TOKEN_TYPE
    is_boi_eoi = torch.zeros_like(x, dtype=torch.bool)
    is_boi_eoi[:, 1:] |= (tmp[:, 1:] == VISION_TOKEN_TYPE) & (tmp[:, :-1] == LANGUAGE_TOKEN_TYPE)
    is_boi_eoi[:, 0] |= (tmp[:, 0] == VISION_TOKEN_TYPE)
    is_boi_eoi[:, :-1] |= (tmp[:, :-1] == VISION_TOKEN_TYPE) & (tmp[:, 1:] == LANGUAGE_TOKEN_TYPE)
    is_boi_eoi[:, -1] |= (tmp[:, -1] == VISION_TOKEN_TYPE)
    tmp[is_boi_eoi] = LANGUAGE_TOKEN_TYPE
    # final position ids
    y = torch.zeros_like(x, dtype=torch.long)
    y[:, 1:] = (tmp[:, 1:] == LANGUAGE_TOKEN_TYPE) | ((tmp[:, 1:] == VISION_TOKEN_TYPE) & (tmp[:, :-1] == LANGUAGE_TOKEN_TYPE))
    y = y.cumsum(dim=-1)
    return y


class CogVLMModel(CogVLMPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.padding_idx = 128002
        self.vocab_size = config.vocab_size
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList([CogVLMDecoderLayer(config) for _ in range(config.num_hidden_layers)])
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.vision = EVA2CLIPModel(config)

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

    def encode_images(self, images: List[List[torch.Tensor]]) -> torch.Tensor:
        images_list, images = images, []

        images = []
        for image_list in images_list:
            for image in image_list:
                images.append(image)

        images = torch.stack(images)
        images_features = self.vision(images)
        return images_features

    def forward(
            self,
            input_ids: torch.LongTensor = None,
            images: List[List[torch.Tensor]] = None,
            token_type_ids: Optional[torch.LongTensor] = None,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_values: Optional[List[torch.FloatTensor]] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        """take care of image_encode, token_type_ids, position_ids and (attention_mask = None is fine)"""

        if past_key_values is not None:
            pass  # generate mode with past_key_values. the image features are already mapped
        else:
            # not allow for inputs_embeds, because we want to process image feature
            assert input_ids is not None and inputs_embeds is None, f"{input_ids} {inputs_embeds}"
            if not is_empty(images):  # multi-modality
                assert token_type_ids is not None, f"multi-modality requires `token_type_ids`!"
                assert len(input_ids) == len(images), f"{len(input_ids)} {len(images)}"
                inputs_embeds = self.embed_tokens(input_ids)
                images_features = self.encode_images(images)
                images_features = rearrange(images_features, 'b n d -> (b n) d')
                images_features = images_features.to(dtype=inputs_embeds.dtype, device=inputs_embeds.device)
                inputs_embeds = inputs_embeds.index_put([token_type_ids == VISION_TOKEN_TYPE], images_features)
            else:  # single-modality
                if token_type_ids is None:
                    token_type_ids = torch.ones_like(input_ids, dtype=torch.long, device=input_ids.device) * LANGUAGE_TOKEN_TYPE
                assert not (token_type_ids == VISION_TOKEN_TYPE).any(), f"{(token_type_ids == VISION_TOKEN_TYPE).sum()}"
                inputs_embeds = self.embed_tokens(input_ids)

            if position_ids is None:
                position_ids = build_position_ids(token_type_ids, attention_mask)
            input_ids = None
        return self.llm_forward(
            input_ids=input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

    def llm_forward(
            self,
            input_ids: torch.LongTensor = None,
            token_type_ids: torch.LongTensor = None,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_values: Optional[List[torch.FloatTensor]] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        """largely copy from llama forward and adapt for cogvlm with `token_type_ids`"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

        seq_length_with_past = seq_length
        past_key_values_length = 0

        if past_key_values is not None:
            past_key_values = past_key_values[1] if isinstance(past_key_values, tuple) else past_key_values
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
        else:
            position_ids = position_ids.view(-1, seq_length).long()

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)
        # embed positions
        if attention_mask is None:
            attention_mask = torch.ones(
                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
            )
        attention_mask = self._prepare_decoder_attention_mask(
            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
        )

        hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if use_cache else None

        for idx, decoder_layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            past_key_value = past_key_values[idx] if past_key_values is not None else None
            
            def custom(index):
                def custom_forward(
                    hidden_states,
                    token_type_ids=token_type_ids,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                ):
                    layer = self.layers[index]
                    outputs = layer(
                        hidden_states,
                        token_type_ids=token_type_ids,
                        attention_mask=attention_mask,
                        position_ids=position_ids,
                        past_key_value=past_key_value,
                        output_attentions=output_attentions,
                        use_cache=use_cache,
                    )
                    return outputs

                return custom_forward
            # layer_outputs = decoder_layer(
            #     hidden_states,
            #     token_type_ids=token_type_ids,
            #     attention_mask=attention_mask,
            #     position_ids=position_ids,
            #     past_key_value=past_key_value,
            #     output_attentions=output_attentions,
            #     use_cache=use_cache,
            # )
            layer_outputs = checkpoint(custom(idx),
                hidden_states,
                use_reentrant=False
            )
            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    # noinspection PyMethodMayBeStatic
    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
        # create causal mask
        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
        combined_attention_mask = None
        if input_shape[-1] > 1:
            combined_attention_mask = _make_causal_mask(
                input_shape,
                inputs_embeds.dtype,
                device=inputs_embeds.device,
                past_key_values_length=past_key_values_length,
            )

        if attention_mask is not None:
            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
                inputs_embeds.device
            )
            combined_attention_mask = (
                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
            )

        return combined_attention_mask


def _history_to_prompt(signal_type, history, query):
    if signal_type == 'base':
        return query
    elif signal_type == 'vqa':
        answer_format = 'Short answer:'
    elif signal_type == 'chat':
        answer_format = 'Answer:'
    else:
        assert False, f"Unknown signal type {signal_type}"

    prompt = ''
    for i, (old_query, response) in enumerate(history):
        prompt += 'Question: ' + old_query + " {} ".format(answer_format) + response + "\n"
    prompt += 'Question: {} {}'.format(query, answer_format)
    return prompt


class CogVLMForCausalLM(CogVLMPreTrainedModel):
    _auto_class = "AutoModelForCausalLM"

    def __init__(self, config):
        super().__init__(config)
        self.model = CogVLMModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    def forward(
            self,
            input_ids: torch.LongTensor = None,
            images: List[List[torch.Tensor]] = None,
            token_type_ids: Optional[torch.LongTensor] = None,
            attention_mask: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            past_key_values: Optional[List[torch.FloatTensor]] = None,
            inputs_embeds: Optional[torch.FloatTensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            labels: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            images=images,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def _prepare_attention_mask_for_generation(
            self,
            inputs: torch.Tensor,
            pad_token_id: Optional[int],
            eos_token_id: Optional[Union[int, List[int]]],
    ) -> torch.LongTensor:
        return torch.ones(inputs.shape[:2], dtype=torch.long, device=inputs.device)  # type: ignore

    def prepare_inputs_for_generation(
            self, input_ids, token_type_ids, images=None, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        # build position_ids if needed
        position_ids = kwargs.get("position_ids", None)
        if position_ids is None:
            position_ids = build_position_ids(token_type_ids, attention_mask)

        if past_key_values:
            input_ids = input_ids[:, -1:]
            token_type_ids = token_type_ids[:, -1:]
            position_ids = position_ids[:, -1:]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "token_type_ids": token_type_ids,
                "images": images,
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    def _update_model_kwargs_for_generation(
            self,
            outputs: "ModelOutput",
            model_kwargs: Dict[str, Any],
            is_encoder_decoder: bool = False,
            standardize_cache_format: bool = False,
    ) -> Dict[str, Any]:
        # update past_key_values
        model_kwargs["past_key_values"] = self._extract_past_from_model_output(
            outputs, standardize_cache_format=standardize_cache_format
        )
        if getattr(outputs, "state", None) is not None:
            model_kwargs["state"] = outputs.state

        # update token_type_ids with last value
        if "token_type_ids" in model_kwargs:
            token_type_ids = model_kwargs["token_type_ids"]
            new_token_type_ids = torch.ones(size=(token_type_ids.shape[0], 1), dtype=token_type_ids.dtype, device=token_type_ids.device) * LANGUAGE_TOKEN_TYPE
            model_kwargs["token_type_ids"] = torch.cat([token_type_ids, new_token_type_ids], dim=-1)

        if not is_encoder_decoder:
            # update attention mask
            if "attention_mask" in model_kwargs:
                attention_mask = model_kwargs["attention_mask"]
                model_kwargs["attention_mask"] = torch.cat(
                    [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
                )
        else:
            # update decoder attention mask
            if "decoder_attention_mask" in model_kwargs:
                decoder_attention_mask = model_kwargs["decoder_attention_mask"]
                model_kwargs["decoder_attention_mask"] = torch.cat(
                    [decoder_attention_mask, decoder_attention_mask.new_ones((decoder_attention_mask.shape[0], 1))],
                    dim=-1,
                )

        return model_kwargs

    def _reorder_cache(self, past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past

    def build_conversation_input_ids(
            self,
            tokenizer: "PreTrainedTokenizer",
            *,
            query: str,
            history: Optional[List[Tuple[str, str]]] = None,
            images: Optional[List["PIL.Image"]] = None,
            template_version: Optional[Literal["base", "chat", "vqa"]] = None,
            answer: str = None,
    ):
        image_size: int = self.config.vision_config['image_size']
        patch_size: int = self.config.vision_config['patch_size']
        template_version = template_version or self.config.template_version
        assert images is None or len(images) <= 1, f"not support multi images by now."
        history = history or []
        text = _history_to_prompt(template_version, history, query)
        input_ids = [tokenizer.bos_token_id]
        token_type_ids = [LANGUAGE_TOKEN_TYPE]
        if images is not None and len(images) == 1:
            # vision
            transform = transforms.Compose(
                [
                    transforms.Resize(
                        (image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC
                    ),
                    transforms.ToTensor(),
                    transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
                ]
            )
            images = [transform(images[0])]
            # language
            vision_token_num = (image_size // patch_size // 2) * (image_size // patch_size // 2) + 2

            tokenizer.pad_token_id = 128002 # llama3 adapt for cogvlm

            input_ids += [tokenizer.pad_token_id] * vision_token_num
            token_type_ids += [VISION_TOKEN_TYPE] * vision_token_num
        text_ids = tokenizer.encode(text, add_special_tokens=False)

        if answer is not None:
            answer_ids = tokenizer.encode(answer, add_special_tokens=False)
            answer_ids += [tokenizer.eos_token_id]
            text_ids += answer_ids


        input_ids += text_ids
        token_type_ids += [LANGUAGE_TOKEN_TYPE] * len(text_ids)
        attention_mask = [1] * len(input_ids)
        if answer is not None:
            labels = [-100 for _ in range(len(input_ids) - len(answer_ids))] + answer_ids
            labels = torch.tensor(labels, dtype=torch.long)
        else:
            labels = None

        return {
            'input_ids': torch.tensor(input_ids, dtype=torch.long),
            'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long),
            'attention_mask': torch.tensor(attention_mask, dtype=torch.long),
            'images': images,
            'labels': labels,
        }
